{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7ab18d1b",
   "metadata": {},
   "source": [
    "| [03_data_science/04_Matpoltlib画图.ipynb](https://github.com/shibing624/python-tutorial/blob/master/03_data_science/04_Matpoltlib画图.ipynb)  | Matpoltlib画图  |[Open In Colab](https://colab.research.google.com/github/shibing624/python-tutorial/blob/master/03_data_science/04_Matpoltlib画图.ipynb) |\n",
    "\n",
    "# 画图：pyplot\n",
    "\n",
    "画图，引入matplotlib库的pyplot方法：\n",
    "\n",
    "\n",
    "linspace 用来生成一组等间隔的数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "18ce7716",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.6981317 , 1.3962634 , 2.0943951 , 2.7925268 ,\n",
       "       3.4906585 , 4.1887902 , 4.88692191, 5.58505361, 6.28318531])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "a = np.linspace(0, 2 * np.pi, 10)\n",
    "a\n",
    "# [ 0.          0.6981317   1.3962634   2.0943951   2.7925268   3.4906585\n",
    "#   4.1887902   4.88692191  5.58505361  6.28318531]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "66b7c4b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00000000e+00,  6.42787610e-01,  9.84807753e-01,  8.66025404e-01,\n",
       "        3.42020143e-01, -3.42020143e-01, -8.66025404e-01, -9.84807753e-01,\n",
       "       -6.42787610e-01, -2.44929360e-16])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 三角函数\n",
    "b = np.sin(a)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b981c159",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(a, b)\n",
    "plt.show() # 正弦图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d4abe627",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function plot in module matplotlib.pyplot:\n",
      "\n",
      "plot(*args, scalex=True, scaley=True, data=None, **kwargs)\n",
      "    Plot y versus x as lines and/or markers.\n",
      "    \n",
      "    Call signatures::\n",
      "    \n",
      "        plot([x], y, [fmt], *, data=None, **kwargs)\n",
      "        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)\n",
      "    \n",
      "    The coordinates of the points or line nodes are given by *x*, *y*.\n",
      "    \n",
      "    The optional parameter *fmt* is a convenient way for defining basic\n",
      "    formatting like color, marker and linestyle. It's a shortcut string\n",
      "    notation described in the *Notes* section below.\n",
      "    \n",
      "    >>> plot(x, y)        # plot x and y using default line style and color\n",
      "    >>> plot(x, y, 'bo')  # plot x and y using blue circle markers\n",
      "    >>> plot(y)           # plot y using x as index array 0..N-1\n",
      "    >>> plot(y, 'r+')     # ditto, but with red plusses\n",
      "    \n",
      "    You can use `.Line2D` properties as keyword arguments for more\n",
      "    control on the appearance. Line properties and *fmt* can be mixed.\n",
      "    The following two calls yield identical results:\n",
      "    \n",
      "    >>> plot(x, y, 'go--', linewidth=2, markersize=12)\n",
      "    >>> plot(x, y, color='green', marker='o', linestyle='dashed',\n",
      "    ...      linewidth=2, markersize=12)\n",
      "    \n",
      "    When conflicting with *fmt*, keyword arguments take precedence.\n",
      "    \n",
      "    \n",
      "    **Plotting labelled data**\n",
      "    \n",
      "    There's a convenient way for plotting objects with labelled data (i.e.\n",
      "    data that can be accessed by index ``obj['y']``). Instead of giving\n",
      "    the data in *x* and *y*, you can provide the object in the *data*\n",
      "    parameter and just give the labels for *x* and *y*::\n",
      "    \n",
      "    >>> plot('xlabel', 'ylabel', data=obj)\n",
      "    \n",
      "    All indexable objects are supported. This could e.g. be a `dict`, a\n",
      "    `pandas.DataFrame` or a structured numpy array.\n",
      "    \n",
      "    \n",
      "    **Plotting multiple sets of data**\n",
      "    \n",
      "    There are various ways to plot multiple sets of data.\n",
      "    \n",
      "    - The most straight forward way is just to call `plot` multiple times.\n",
      "      Example:\n",
      "    \n",
      "      >>> plot(x1, y1, 'bo')\n",
      "      >>> plot(x2, y2, 'go')\n",
      "    \n",
      "    - Alternatively, if your data is already a 2d array, you can pass it\n",
      "      directly to *x*, *y*. A separate data set will be drawn for every\n",
      "      column.\n",
      "    \n",
      "      Example: an array ``a`` where the first column represents the *x*\n",
      "      values and the other columns are the *y* columns::\n",
      "    \n",
      "      >>> plot(a[0], a[1:])\n",
      "    \n",
      "    - The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*\n",
      "      groups::\n",
      "    \n",
      "      >>> plot(x1, y1, 'g^', x2, y2, 'g-')\n",
      "    \n",
      "      In this case, any additional keyword argument applies to all\n",
      "      datasets. Also this syntax cannot be combined with the *data*\n",
      "      parameter.\n",
      "    \n",
      "    By default, each line is assigned a different style specified by a\n",
      "    'style cycle'. The *fmt* and line property parameters are only\n",
      "    necessary if you want explicit deviations from these defaults.\n",
      "    Alternatively, you can also change the style cycle using\n",
      "    :rc:`axes.prop_cycle`.\n",
      "    \n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y : array-like or scalar\n",
      "        The horizontal / vertical coordinates of the data points.\n",
      "        *x* values are optional and default to ``range(len(y))``.\n",
      "    \n",
      "        Commonly, these parameters are 1D arrays.\n",
      "    \n",
      "        They can also be scalars, or two-dimensional (in that case, the\n",
      "        columns represent separate data sets).\n",
      "    \n",
      "        These arguments cannot be passed as keywords.\n",
      "    \n",
      "    fmt : str, optional\n",
      "        A format string, e.g. 'ro' for red circles. See the *Notes*\n",
      "        section for a full description of the format strings.\n",
      "    \n",
      "        Format strings are just an abbreviation for quickly setting\n",
      "        basic line properties. All of these and more can also be\n",
      "        controlled by keyword arguments.\n",
      "    \n",
      "        This argument cannot be passed as keyword.\n",
      "    \n",
      "    data : indexable object, optional\n",
      "        An object with labelled data. If given, provide the label names to\n",
      "        plot in *x* and *y*.\n",
      "    \n",
      "        .. note::\n",
      "            Technically there's a slight ambiguity in calls where the\n",
      "            second label is a valid *fmt*. ``plot('n', 'o', data=obj)``\n",
      "            could be ``plt(x, y)`` or ``plt(y, fmt)``. In such cases,\n",
      "            the former interpretation is chosen, but a warning is issued.\n",
      "            You may suppress the warning by adding an empty format string\n",
      "            ``plot('n', 'o', '', data=obj)``.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    list of `.Line2D`\n",
      "        A list of lines representing the plotted data.\n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    scalex, scaley : bool, default: True\n",
      "        These parameters determine if the view limits are adapted to the\n",
      "        data limits. The values are passed on to `autoscale_view`.\n",
      "    \n",
      "    **kwargs : `.Line2D` properties, optional\n",
      "        *kwargs* are used to specify properties like a line label (for\n",
      "        auto legends), linewidth, antialiasing, marker face color.\n",
      "        Example::\n",
      "    \n",
      "        >>> plot([1, 2, 3], [1, 2, 3], 'go-', label='line 1', linewidth=2)\n",
      "        >>> plot([1, 2, 3], [1, 4, 9], 'rs', label='line 2')\n",
      "    \n",
      "        If you make multiple lines with one plot call, the kwargs\n",
      "        apply to all those lines.\n",
      "    \n",
      "        Here is a list of available `.Line2D` properties:\n",
      "    \n",
      "        Properties:\n",
      "        agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array\n",
      "        alpha: float or None\n",
      "        animated: bool\n",
      "        antialiased or aa: bool\n",
      "        clip_box: `.Bbox`\n",
      "        clip_on: bool\n",
      "        clip_path: Patch or (Path, Transform) or None\n",
      "        color or c: color\n",
      "        contains: unknown\n",
      "        dash_capstyle: {'butt', 'round', 'projecting'}\n",
      "        dash_joinstyle: {'miter', 'round', 'bevel'}\n",
      "        dashes: sequence of floats (on/off ink in points) or (None, None)\n",
      "        data: (2, N) array or two 1D arrays\n",
      "        drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'\n",
      "        figure: `.Figure`\n",
      "        fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}\n",
      "        gid: str\n",
      "        in_layout: bool\n",
      "        label: object\n",
      "        linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n",
      "        linewidth or lw: float\n",
      "        marker: marker style string, `~.path.Path` or `~.markers.MarkerStyle`\n",
      "        markeredgecolor or mec: color\n",
      "        markeredgewidth or mew: float\n",
      "        markerfacecolor or mfc: color\n",
      "        markerfacecoloralt or mfcalt: color\n",
      "        markersize or ms: float\n",
      "        markevery: None or int or (int, int) or slice or List[int] or float or (float, float) or List[bool]\n",
      "        path_effects: `.AbstractPathEffect`\n",
      "        picker: unknown\n",
      "        pickradius: float\n",
      "        rasterized: bool or None\n",
      "        sketch_params: (scale: float, length: float, randomness: float)\n",
      "        snap: bool or None\n",
      "        solid_capstyle: {'butt', 'round', 'projecting'}\n",
      "        solid_joinstyle: {'miter', 'round', 'bevel'}\n",
      "        transform: `matplotlib.transforms.Transform`\n",
      "        url: str\n",
      "        visible: bool\n",
      "        xdata: 1D array\n",
      "        ydata: 1D array\n",
      "        zorder: float\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    scatter : XY scatter plot with markers of varying size and/or color (\n",
      "        sometimes also called bubble chart).\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    **Format Strings**\n",
      "    \n",
      "    A format string consists of a part for color, marker and line::\n",
      "    \n",
      "        fmt = '[marker][line][color]'\n",
      "    \n",
      "    Each of them is optional. If not provided, the value from the style\n",
      "    cycle is used. Exception: If ``line`` is given, but no ``marker``,\n",
      "    the data will be a line without markers.\n",
      "    \n",
      "    Other combinations such as ``[color][marker][line]`` are also\n",
      "    supported, but note that their parsing may be ambiguous.\n",
      "    \n",
      "    **Markers**\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\n",
      "    ``'.'``          point marker\n",
      "    ``','``          pixel marker\n",
      "    ``'o'``          circle marker\n",
      "    ``'v'``          triangle_down marker\n",
      "    ``'^'``          triangle_up marker\n",
      "    ``'<'``          triangle_left marker\n",
      "    ``'>'``          triangle_right marker\n",
      "    ``'1'``          tri_down marker\n",
      "    ``'2'``          tri_up marker\n",
      "    ``'3'``          tri_left marker\n",
      "    ``'4'``          tri_right marker\n",
      "    ``'s'``          square marker\n",
      "    ``'p'``          pentagon marker\n",
      "    ``'*'``          star marker\n",
      "    ``'h'``          hexagon1 marker\n",
      "    ``'H'``          hexagon2 marker\n",
      "    ``'+'``          plus marker\n",
      "    ``'x'``          x marker\n",
      "    ``'D'``          diamond marker\n",
      "    ``'d'``          thin_diamond marker\n",
      "    ``'|'``          vline marker\n",
      "    ``'_'``          hline marker\n",
      "    =============    ===============================\n",
      "    \n",
      "    **Line Styles**\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\n",
      "    ``'-'``          solid line style\n",
      "    ``'--'``         dashed line style\n",
      "    ``'-.'``         dash-dot line style\n",
      "    ``':'``          dotted line style\n",
      "    =============    ===============================\n",
      "    \n",
      "    Example format strings::\n",
      "    \n",
      "        'b'    # blue markers with default shape\n",
      "        'or'   # red circles\n",
      "        '-g'   # green solid line\n",
      "        '--'   # dashed line with default color\n",
      "        '^k:'  # black triangle_up markers connected by a dotted line\n",
      "    \n",
      "    **Colors**\n",
      "    \n",
      "    The supported color abbreviations are the single letter codes\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        color\n",
      "    =============    ===============================\n",
      "    ``'b'``          blue\n",
      "    ``'g'``          green\n",
      "    ``'r'``          red\n",
      "    ``'c'``          cyan\n",
      "    ``'m'``          magenta\n",
      "    ``'y'``          yellow\n",
      "    ``'k'``          black\n",
      "    ``'w'``          white\n",
      "    =============    ===============================\n",
      "    \n",
      "    and the ``'CN'`` colors that index into the default property cycle.\n",
      "    \n",
      "    If the color is the only part of the format string, you can\n",
      "    additionally use any  `matplotlib.colors` spec, e.g. full names\n",
      "    (``'green'``) or hex strings (``'#008000'``).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "336656fc",
   "metadata": {},
   "source": [
    "从数组中选择元素：\n",
    "\n",
    "假设我们想选取数组 b 中所有非负的部分，首先可以利用 b 产生一组布尔值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3c0d91be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True, False, False, False, False,\n",
       "       False])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = b >= 0\n",
    "mask"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "673c6b57",
   "metadata": {},
   "source": [
    "更密集的数据，更平滑的正弦曲线："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9e8f4878",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.12822827, 0.25645654, 0.38468481, 0.51291309,\n",
       "       0.64114136, 0.76936963, 0.8975979 , 1.02582617, 1.15405444,\n",
       "       1.28228272, 1.41051099, 1.53873926, 1.66696753, 1.7951958 ,\n",
       "       1.92342407, 2.05165235, 2.17988062, 2.30810889, 2.43633716,\n",
       "       2.56456543, 2.6927937 , 2.82102197, 2.94925025, 3.07747852,\n",
       "       3.20570679, 3.33393506, 3.46216333, 3.5903916 , 3.71861988,\n",
       "       3.84684815, 3.97507642, 4.10330469, 4.23153296, 4.35976123,\n",
       "       4.48798951, 4.61621778, 4.74444605, 4.87267432, 5.00090259,\n",
       "       5.12913086, 5.25735913, 5.38558741, 5.51381568, 5.64204395,\n",
       "       5.77027222, 5.89850049, 6.02672876, 6.15495704, 6.28318531])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(0, 2 * np.pi, 50)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "48a593d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.sin(x))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "58010aa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 给定 x 和 y 值：\n",
    "plt.plot(x, np.sin(x))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a0cf2a55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出多条数据线：\n",
    "plt.plot(x, np.sin(x), x, np.sin(2 * x))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e51c382f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用字符串，给定线条参数：\n",
    "plt.plot(x, np.sin(x), 'r-^')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7a69a86d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 多线条：\n",
    "plt.plot(x, np.sin(x), 'b-o', x, np.sin(2 * x), 'r-^')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfa72f47",
   "metadata": {},
   "source": [
    "### 散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0c9b804d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x), 'bo')\n",
    "plt.show()  # 二维散点图"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54728155",
   "metadata": {},
   "source": [
    "用scatter画图，scatter 散点图\n",
    "\n",
    "- scatter(x, y)\n",
    "- scatter(x, y, size)\n",
    "- scatter(x, y, size, color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "14c435e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, np.sin(x))\n",
    "plt.show()  # 正弦函数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d54b3fa3",
   "metadata": {},
   "source": [
    "事实上，scatter函数与Matlab的用法相同，还可以指定它的大小，颜色等参数。\n",
    "\n",
    "### 标签：label\n",
    "\n",
    "可以在 plot 中加入 label ，使用 legend 加上图例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "51b3219a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.linspace(0, 2 * np.pi, 50)\n",
    "x = np.sin(t)\n",
    "plt.plot(t, x, 'bo', t, np.sin(2 * t), 'r-^', label='sin', color='red', )\n",
    "plt.legend()\n",
    "plt.xlabel('radians')\n",
    "plt.ylabel('amplitude', fontsize='large')\n",
    "plt.title('Sin(x)')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e1849041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 直方图\n",
    "data = np.array([1234, 321, 400, 120, 11, 30, 2000])\n",
    "plt.hist(data, 7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb70da00",
   "metadata": {},
   "source": [
    "本节完。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}